Dynamic changes and multi-dimensional evolution of portfolio optimization

Abstract

Although there has been an increasing number of studies investigate portfolio optimization from different perspectives, few attempts could be found that focus on the development trend and hotspots of this research area. Therefore, it motivates us to comprehensively investigate the development of portfolio optimization research and give some deep insights into this knowledge domain. In this paper, some bibliometric methods are utilized to analyse the status quo and emerging trends of portfolio optimization research on various aspects such as authors, countries and journals. Besides, 'theories', 'models' and 'algorithms', especially heuristic algorithms are identified as the hotspots in the given periods. Furthermore, the evolutionary analysis tends to presents the dynamic changes of the cutting-edge concepts of this research area in the time dimension. It is found that more portfolio optimization studies were at an exploration stage from mean-variance analysis to consideration of multiple constraints. However, heuristic algorithms have become the driving force of portfolio optimization research in recent years. Multidisciplinary analyses and applications are also the main trends of portfolio optimization research. By analysing the dynamic changes and multi-dimensional evolution in recent decades, we contribute to presenting some deep insights of the portfolio optimization research directly, which assists researchers especially beginners to comprehensively learn this research field.

Portfolio optimization based on self-organizing maps clustering and genetics algorithm

“…After the input data has been entered, clustering can be done using the SOM method. The steps for the SOM algorithm are as follows; (1) The initial process is to determine the data that will be used as input (xi); (2) Initialize the weights of wij, α0, σ0 randomly; (3) For each xi perform steps 4 through 6; (4) For every j count all the values of D(j); (5) Determine the value of j such that D(j) is minimum; (6) Update the weights of the winning and neighboring neurons; (7) Update learning rate and neighbor function. Test the stop conditions, perform steps 2 through 7 if the stop conditions are not met.…”

“…The greater the risk faced by an investor, the greater the rate of return that investor will get. There are several types of investors in facing risk, namely, riskseeking investors, risk-neutral investors, and risk-averse investors [1]. In addition, the investment strategy also needs to be considered, for example, in the portfolio construction.…”

A Multistage Stochastic Programming Model with Multiple Objectives for the Optimal Issuance of Corporate Bonds

“…Considering the multistage multiobjective bond portfolio optimization is the main direction of the current multiobjective bond portfolio optimization evolutionary algorithm. In multiclass combinatorial optimization problems, decision-makers perform multiobjective (Pareto) optimization in multiple stages can explore various reasonable and effective optimal solutions in complex situations, and strive to develop algorithms that minimize the computational burden (Balıbek, Banihashemi, Radulescu et al, Zhou et al, Kim et al,) [28][29][30][31][32]. Ma et al considered multiple objectives and complex constraint problems at the same time and explored that most of the current multiobjective optimization problems in complex constraint regions are inefficient and have insufficient convergence.…”

Comparison of Portfolio Mean-Variance Method with the Mean-Variance-Skewness-Kurtosis Method in Indonesia Stocks

“…A portfolio is a combination of several assets or instruments formed by investors with the aim of obtaining profits (returns) in the future. The portfolio chosen by investors depends on their preference for return and the risk they desire [1]. To build an optimal portfolio, investors can choose one from the many options available in a set of efficient portfolios [2] [3].…”